In the fast-paced world of technology, businesses are constantly seeking ways to enhance their operations and deliver superior customer experiences. This demand has led technology teams to innovate solutions that not only accelerate time-to-market but also optimize operational expenses. By lowering operational costs, companies can reinvest those savings into improved services or competitive pricing, ultimately attracting more clients and boosting revenue. Adopting a cloud-native strategy provides organizations with a competitive edge by enhancing efficiency, minimizing costs, and ensuring reliability.
In this article, we delve into a case study of a company grappling with an outdated file ingestion and processing framework that had served them for a decade. With their business expanding, this legacy system could no longer meet the growing demands, leading to significant operational issues and escalating maintenance costs.
Tech Innovations, an AWS Partner, comprises a team of data specialists committed to assisting enterprises that rely heavily on data. Their mission is to empower clients to effectively collect, transform, and manage their data, paving the way for them to become data-driven organizations.
Challenges with Legacy Systems
The client in question provides inventory management, online catalogs, purchasing, and scheduling services to small and medium-sized manufacturers and distributors. As part of their services, they developed a framework that enabled clients to upload catalogs, bills of materials, quotations, and other documents to an SFTP server. Once uploaded, the framework would parse and transform the data and update the backend database.
Despite its initial effectiveness, the legacy system struggled to keep pace with the company’s growth. Various challenges emerged as the client onboarded more customers:
- Processing time for incoming files increased significantly, leading to stale data before it was refreshed in the database.
- Failures often went unnoticed, resulting in customer dissatisfaction and missed deadlines.
- Modifications to file structures could disrupt the entire framework.
- Capacity planning and operational management became increasingly difficult.
Tech Innovations’ Approach to Operational Challenges
Tech Innovations proposed a solution based on several guiding principles:
- Simplicity: The architecture should minimize operational and maintenance overhead, incorporating built-in monitoring, logging, notifications, and remediation tools.
- Decoupling Processes: With files arriving unpredictably, an on-demand processing strategy was more effective than a scheduled approach, allowing for immediate processing as files were received. Parallel processing was also prioritized.
- Scalability and Cost Efficiency: The design needed to accommodate fluctuating demand while optimizing costs and maintaining security.
To execute this vision, Tech Innovations opted for a serverless architecture leveraging AWS services. This transformation enabled the client to easily onboard thousands of new customers without concerns about infrastructure scaling or server maintenance. The cloud business optimization team also helped evaluate and replace several costly proprietary software licenses with AWS services, achieving a remarkable 63% cost savings. Furthermore, they guided the migration from a monolithic application to a microservices architecture, allowing for flexible updates to file parsers with minimal disruption.
Modern Serverless Architecture Overview
The new architecture includes several key components:
- AWS Transfer Family serves as the SFTP provider, allowing users to upload files seamlessly without changing their existing processes.
- Files are stored in an Amazon S3 bucket, which triggers an event that sends a message to an Amazon SQS queue whenever a file is uploaded. This setup prevents errors from occurring when multiple files arrive simultaneously.
- An Amazon EventBridge rule activates an AWS Step Function, which orchestrates the workflow.
- The workflow begins with an AWS Lambda function that logs file information into an Amazon DynamoDB table for analytics and error handling.
- Separate queues, crawlers, and jobs are established for each file type, allowing parallel processing of diverse files.
- The Glue crawler ensures files are processed correctly and updates the Glue catalog accordingly, followed by transformations via an AWS Glue job.
- Finally, processed data is loaded into an Amazon Aurora Serverless database that supports the web application, which is deployed using AWS Amplify for ease of management.
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